Autonomous driving and obstacle avoidance for car-like vehicles

• Κύριος συγγραφέας: Κατσανικάκης, Ανδρέας
• Άλλοι συγγραφείς: Katsanikakis, Andreas
• Γλώσσα: English
• Έκδοση: 2022
• Θέματα: Autonomous vehicles; Autonomous driving; Autonomous navigation; Trajectory tracking; Collision avoidance; Obstacle avoidance; CoppeliaSim; Αυτόνομη πλοήγηση; Αυτόνομα οχήματα; Παρακολούθηση τροχιάς; Αποφυγή σύγκρουσης; Αποφυγή εμποδίων; Αυτόνομη οδήγηση

Autonomous navigation systems play a significant role in every aspect of the technological development humanity has known recently. A robot or a vehicle has the ability to determine its location and plan a path to some destination without human interference. This statement, by its own, indicates that autonomous technology can derive great scientific results since these systems can be incorporated in projects that a human could not achieve alone up until now. Automobiles, space, health, sports, food and many more industries invest more and more funds daily to develop autonomous systems for their own use. Especially in automobiles, autonomous vehicles can change the entirely operation of the whole road traffic network. The statement of a world that can travel autonomously is now a project that can become reality. The following thesis focuses on simulating a driving car scenario where a vehicle navigates autonomously inside a road scene. Obstacles appear throughout its course, and it is anticipated to avoid them while at the same time not diverging from the road boundaries. Trajectory tracking and collision avoidance techniques are developed and a main algorithm that completes those tasks is proposed. The correct functionality of the algorithm is firstly tested in Simulink/MATLAB software and then the simulation takes place in the robotic-based CoppeliaSim Edu software. The simulation is based mostly on kinematic theory. The scenario, the road scene, the model that has been adopted for the vehicle, and the obstacles that are used in the simulator try to approach a real case perspective. Results are presented in graphs and further discussion is made.